@Article{info:doi/10.2196/17643,
author="Wang, Erniu
and Wang, Fan
and Yang, Zhihao
and Wang, Lei
and Zhang, Yin
and Lin, Hongfei
and Wang, Jian",
title="A Graph Convolutional Network--Based Method for Chemical-Protein Interaction Extraction: Algorithm Development",
journal="JMIR Med Inform",
year="2020",
month="May",
day="19",
volume="8",
number="5",
pages="e17643",
keywords="chemical-protein interaction; graph convolutional network; long-range syntactic; dependency structure",
abstract="Background: Extracting the interactions between chemicals and proteins from the biomedical literature is important for many biomedical tasks such as drug discovery, medicine precision, and knowledge graph construction. Several computational methods have been proposed for automatic chemical-protein interaction (CPI) extraction. However, the majority of these proposed models cannot effectively learn semantic and syntactic information from complex sentences in biomedical texts. Objective: To relieve this problem, we propose a method to effectively encode syntactic information from long text for CPI extraction. Methods: Since syntactic information can be captured from dependency graphs, graph convolutional networks (GCNs) have recently drawn increasing attention in natural language processing. To investigate the performance of a GCN on CPI extraction, this paper proposes a novel GCN-based model. The model can effectively capture sequential information and long-range syntactic relations between words by using the dependency structure of input sentences. Results: We evaluated our model on the ChemProt corpus released by BioCreative VI; it achieved an F-score of 65.17{\%}, which is 1.07{\%} higher than that of the state-of-the-art system proposed by Peng et al. As indicated by the significance test (P